The present invention relates to detection of 3D spinal geometry in images, and more particularly, to automated detection and labeling of 3D spinal disks in medical images using iterated marginal space learning.
Examinations of the vertebral column with both Magnetic Resonance (MR) and Computer Tomography (CT) require a standardized alignment of the scan geometry with the spine. While in MR, the intervertebral disks can be used to align slice groups to position saturated bands, in CT the reconstruction planes need to be aligned. In addition to the position and orientation of the disks, physicians are typically interested in labeling the disks (e.g., C2/C3, C5/T1, L1/L2 . . . ). Labeling the intervertebral disks allows one to quickly determine the anatomical location without error-prone counting. As manual alignment is both time consuming and operator dependent, it is desirable to have a robust, fully automatic, and thus reproducible approach for detecting and labeling spinal geometry.
An automatic procedure for extracting the spinal geometry faces various challenges, however. Varying contrasts and image artifacts can compromise the detection of intervertebral disks based on local image features. Thus, a global spinal model is required to robustly identify individual disks from their context. Such a model must also cope with missed detections and subjects with an unusual number of vertebrae. Further, the overall approach should run quickly to allow clinical application.